_base_ = "./mask_rcnn_r101_fpn_1x_coco.py"
model = dict(
    pretrained="open-mmlab://detectron2/resnext101_32x8d",
    backbone=dict(
        type="ResNeXt",
        depth=101,
        groups=32,
        base_width=8,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type="BN", requires_grad=False),
        style="pytorch",
    ),
)

dataset_type = "CocoDataset"
data_root = "data/coco/"
img_norm_cfg = dict(
    mean=[103.530, 116.280, 123.675], std=[57.375, 57.120, 58.395], to_rgb=False
)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True, with_mask=True, poly2mask=False),
    dict(
        type="Resize",
        img_scale=[
            (1333, 640),
            (1333, 672),
            (1333, 704),
            (1333, 736),
            (1333, 768),
            (1333, 800),
        ],
        multiscale_mode="value",
        keep_ratio=True,
    ),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels", "gt_masks"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="Pad", size_divisor=32),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
)

lr_config = dict(step=[28, 34])
runner = dict(type="EpochBasedRunner", max_epochs=36)
